Applied Scientist / Research Engineer — Data Quality & Evaluation
Core
Develop methods for training data curation, quality scoring, dataset selection, and evaluation frameworks to improve advanced AI model performance.
Role type
Applied Scientist / Research Engineer (Data Quality & Evaluation)
Builds
Automated tools and pipelines for data quality improvement and evaluation benchmarks
Domain
Artificial Intelligence / Machine Learning / Data-Centric AI
Deliverable
production ML models
Required skills
Applied ML research, Python, ML frameworks, data processing, experimental design, active learning, human-in-the-loop evaluation, automated pipeline development
Preferred skills
Experience at AI data companies, model performance improvement via data, evaluation framework building, data selection algorithms, research publications
Technologies
Python, ML frameworks, data processing tools
Responsibilities
Develop methods for training data curation and quality scoring, Design evaluation frameworks and benchmarks, Analyze model failures to understand data impact, Apply active learning and human-in-the-loop techniques, Build automated tools to reduce annotation costs, Design and run experiments to measure data improvements
Seniority
Mid-Senior, hands-on IC
